Application of machine learning methods in the analysis of interface bonding strength for overmolded hybrid thermoset‐thermoplastic composites

Abstract The focus of this study is to apply finite element method (FEM) and machine learning methods to investigate the interfacial bonding strength of continuous fiber reinforced thermoset composite (TSC)‐thermoplastic structures manufactured through co‐curing and overmolding processes, with polyamide 6 (PA 6) as the thermoplastic material. A model for interfacial healing degree in TSC‐PA 6 structures was developed. Then, FEM was employed to study the influence of various injection overmolding process parameters on the interfacial bonding strength of TSC‐PA 6 structures. The results show that there is a strong correlation between the degree of interface healing and the bonding strength. Subsequently, six machine learning methods were employed to correlate interfacial healing degree with diverse injection molding process parameters. Simulation data were utilized for training, calibration, and validation of the six machine learning models. Based on the results of simulation and machine learning predictions, a quantitative analysis of the significance of injection molding process parameters on healing degree was conducted. These parameters are ranked in descending order of importance as follows: insert temperature, melt temperature, and injection rate. Highlights The interfacial healing degree model of PA 6 was established and validated through the integration FEM and experiment. Six machine learning models were built to predict the interfacial healing degree. Grad boosting performed best in predicting the interfacial healing degree. Insert temperature had the greatest impact on the interface healing degree.